{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8-final"
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  "kernelspec": {
   "name": "python388jvsc74a57bd0238245627cfcfafd0885ed67ad6caa77217e3abcf58754ab647d5e61878a8b28",
   "display_name": "Python 3.8.8 64-bit ('tensorflow': conda)"
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 },
 "nbformat": 4,
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(1000, 3)\n(673, 3)\n"
     ]
    }
   ],
   "source": [
    "# 加载数据集\n",
    "data = pd.read_csv(\"../data/testdata/classification.txt\",delimiter=\"\\t\",names=[\"first\",\"second\",\"third\",\"forth\"],header=None)\n",
    "data.head()\n",
    "x = data.iloc[:,:-1]\n",
    "y = data.iloc[:,-1]\n",
    "print(x.shape)\n",
    "is_ok = y!=3\n",
    "x=x[is_ok]\n",
    "y=y[is_ok]-1\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense (Dense)                (None, 4)                 16        \n_________________________________________________________________\ndense_1 (Dense)              (None, 4)                 20        \n_________________________________________________________________\ndense_2 (Dense)              (None, 1)                 5         \n=================================================================\nTotal params: 41\nTrainable params: 41\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(4,input_shape=(3,),activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(4,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "    loss = 'binary_crossentropy',\n",
    "    metrics = 'acc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/100\n",
      "22/22 [==============================] - 0s 860us/step - loss: 1999.9047 - acc: 0.4844\n",
      "Epoch 2/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 934.4822 - acc: 0.4844\n",
      "Epoch 3/100\n",
      "22/22 [==============================] - 0s 681us/step - loss: 84.6207 - acc: 0.5423\n",
      "Epoch 4/100\n",
      "22/22 [==============================] - 0s 771us/step - loss: 47.1125 - acc: 0.5082\n",
      "Epoch 5/100\n",
      "22/22 [==============================] - 0s 725us/step - loss: 13.2378 - acc: 0.4844\n",
      "Epoch 6/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 2.4253 - acc: 0.5290\n",
      "Epoch 7/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.2217 - acc: 0.5379\n",
      "Epoch 8/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 0.9455 - acc: 0.5572\n",
      "Epoch 9/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 0.5759 - acc: 0.6374\n",
      "Epoch 10/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 1.1966 - acc: 0.5587\n",
      "Epoch 11/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.7187 - acc: 0.5186\n",
      "Epoch 12/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.3341 - acc: 0.5394\n",
      "Epoch 13/100\n",
      "22/22 [==============================] - 0s 952us/step - loss: 0.9289 - acc: 0.6092\n",
      "Epoch 14/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 2.3473 - acc: 0.5438\n",
      "Epoch 15/100\n",
      "22/22 [==============================] - 0s 771us/step - loss: 1.5631 - acc: 0.5587\n",
      "Epoch 16/100\n",
      "22/22 [==============================] - 0s 725us/step - loss: 1.3545 - acc: 0.5736\n",
      "Epoch 17/100\n",
      "22/22 [==============================] - 0s 816us/step - loss: 1.4701 - acc: 0.5676\n",
      "Epoch 18/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 1.8435 - acc: 0.5944\n",
      "Epoch 19/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.6615 - acc: 0.5825\n",
      "Epoch 20/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 1.0157 - acc: 0.5780\n",
      "Epoch 21/100\n",
      "22/22 [==============================] - 0s 725us/step - loss: 1.1981 - acc: 0.5825\n",
      "Epoch 22/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 0.6544 - acc: 0.6612\n",
      "Epoch 23/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 4.1438 - acc: 0.5201\n",
      "Epoch 24/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 2.9462 - acc: 0.5349\n",
      "Epoch 25/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.4139 - acc: 0.5602\n",
      "Epoch 26/100\n",
      "22/22 [==============================] - 0s 544us/step - loss: 1.1328 - acc: 0.5617\n",
      "Epoch 27/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 0.4672 - acc: 0.7623\n",
      "Epoch 28/100\n",
      "22/22 [==============================] - 0s 544us/step - loss: 1.1938 - acc: 0.6612\n",
      "Epoch 29/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.4440 - acc: 0.5706\n",
      "Epoch 30/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 1.7510 - acc: 0.6092\n",
      "Epoch 31/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 1.7891 - acc: 0.6048\n",
      "Epoch 32/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 3.5647 - acc: 0.5661\n",
      "Epoch 33/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 2.8371 - acc: 0.5721\n",
      "Epoch 34/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 2.3344 - acc: 0.5676\n",
      "Epoch 35/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 1.9797 - acc: 0.5736\n",
      "Epoch 36/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 5.4850 - acc: 0.5468\n",
      "Epoch 37/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 3.1920 - acc: 0.5706\n",
      "Epoch 38/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 0.6792 - acc: 0.7103\n",
      "Epoch 39/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.3275 - acc: 0.6330\n",
      "Epoch 40/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 4.1723 - acc: 0.5394\n",
      "Epoch 41/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 1.7950 - acc: 0.6137\n",
      "Epoch 42/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 2.0244 - acc: 0.6077\n",
      "Epoch 43/100\n",
      "22/22 [==============================] - 0s 771us/step - loss: 1.1673 - acc: 0.6404\n",
      "Epoch 44/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.6244 - acc: 0.6419\n",
      "Epoch 45/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 1.3646 - acc: 0.6939\n",
      "Epoch 46/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 2.0528 - acc: 0.6166\n",
      "Epoch 47/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 2.0164 - acc: 0.6270\n",
      "Epoch 48/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.1594 - acc: 0.6478\n",
      "Epoch 49/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 0.8200 - acc: 0.7132\n",
      "Epoch 50/100\n",
      "22/22 [==============================] - 0s 861us/step - loss: 1.1367 - acc: 0.6419\n",
      "Epoch 51/100\n",
      "22/22 [==============================] - 0s 952us/step - loss: 4.9606 - acc: 0.5483\n",
      "Epoch 52/100\n",
      "22/22 [==============================] - 0s 861us/step - loss: 1.6338 - acc: 0.6018\n",
      "Epoch 53/100\n",
      "22/22 [==============================] - 0s 952us/step - loss: 0.6635 - acc: 0.7192\n",
      "Epoch 54/100\n",
      "22/22 [==============================] - 0s 816us/step - loss: 1.7348 - acc: 0.5869\n",
      "Epoch 55/100\n",
      "22/22 [==============================] - 0s 952us/step - loss: 0.5009 - acc: 0.7964\n",
      "Epoch 56/100\n",
      "22/22 [==============================] - 0s 861us/step - loss: 14.4451 - acc: 0.5171\n",
      "Epoch 57/100\n",
      "22/22 [==============================] - 0s 997us/step - loss: 13.3722 - acc: 0.5290\n",
      "Epoch 58/100\n",
      "22/22 [==============================] - 0s 861us/step - loss: 9.1949 - acc: 0.5423\n",
      "Epoch 59/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.4300 - acc: 0.6582\n",
      "Epoch 60/100\n",
      "22/22 [==============================] - 0s 725us/step - loss: 1.1293 - acc: 0.7028\n",
      "Epoch 61/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 0.5554 - acc: 0.7890\n",
      "Epoch 62/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 6.9586 - acc: 0.5334\n",
      "Epoch 63/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 4.1818 - acc: 0.5305\n",
      "Epoch 64/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.2039 - acc: 0.6850\n",
      "Epoch 65/100\n",
      "22/22 [==============================] - 0s 861us/step - loss: 1.0174 - acc: 0.6850\n",
      "Epoch 66/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 1.7052 - acc: 0.6137\n",
      "Epoch 67/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 0.7433 - acc: 0.7043\n",
      "Epoch 68/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.5683 - acc: 0.6464\n",
      "Epoch 69/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 1.2583 - acc: 0.6523\n",
      "Epoch 70/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.8944 - acc: 0.6285\n",
      "Epoch 71/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 2.4381 - acc: 0.5899\n",
      "Epoch 72/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 3.4898 - acc: 0.5750\n",
      "Epoch 73/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 2.0402 - acc: 0.6137\n",
      "Epoch 74/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 0.5568 - acc: 0.7415\n",
      "Epoch 75/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 0.9460 - acc: 0.6657\n",
      "Epoch 76/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 0.6263 - acc: 0.7578\n",
      "Epoch 77/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 0.6239 - acc: 0.7682\n",
      "Epoch 78/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 0.2093 - acc: 0.9406\n",
      "Epoch 79/100\n",
      "22/22 [==============================] - 0s 997us/step - loss: 0.1831 - acc: 0.9569\n",
      "Epoch 80/100\n",
      "22/22 [==============================] - 0s 816us/step - loss: 0.1953 - acc: 0.9406\n",
      "Epoch 81/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 1.3981 - acc: 0.6523\n",
      "Epoch 82/100\n",
      "22/22 [==============================] - 0s 725us/step - loss: 0.6550 - acc: 0.7474\n",
      "Epoch 83/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.1610 - acc: 0.6954\n",
      "Epoch 84/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.3426 - acc: 0.6746\n",
      "Epoch 85/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 3.5396 - acc: 0.5691\n",
      "Epoch 86/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 2.0360 - acc: 0.6092\n",
      "Epoch 87/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.6862 - acc: 0.6523\n",
      "Epoch 88/100\n",
      "22/22 [==============================] - 0s 680us/step - loss: 1.4879 - acc: 0.7013\n",
      "Epoch 89/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 1.3099 - acc: 0.6716\n",
      "Epoch 90/100\n",
      "22/22 [==============================] - 0s 725us/step - loss: 0.5344 - acc: 0.8068\n",
      "Epoch 91/100\n",
      "22/22 [==============================] - 0s 635us/step - loss: 0.3725 - acc: 0.8291\n",
      "Epoch 92/100\n",
      "22/22 [==============================] - 0s 589us/step - loss: 0.2396 - acc: 0.9034\n",
      "Epoch 93/100\n",
      "22/22 [==============================] - 0s 816us/step - loss: 1.0517 - acc: 0.6627\n",
      "Epoch 94/100\n",
      "22/22 [==============================] - 0s 861us/step - loss: 2.7516 - acc: 0.6196\n",
      "Epoch 95/100\n",
      "22/22 [==============================] - 0s 771us/step - loss: 1.2560 - acc: 0.6285\n",
      "Epoch 96/100\n",
      "22/22 [==============================] - 0s 952us/step - loss: 1.2161 - acc: 0.7028\n",
      "Epoch 97/100\n",
      "22/22 [==============================] - 0s 952us/step - loss: 0.8433 - acc: 0.7340\n",
      "Epoch 98/100\n",
      "22/22 [==============================] - 0s 816us/step - loss: 1.8510 - acc: 0.6568\n",
      "Epoch 99/100\n",
      "22/22 [==============================] - 0s 907us/step - loss: 1.6735 - acc: 0.6434\n",
      "Epoch 100/100\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.8012 - acc: 0.7281\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x,y,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "history.history.keys()\n",
    "plt.plot(history.epoch,history.history.get('loss'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15c71527940>]"
      ]
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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KFZuiWnT/5DKeP7mE16ZWTVufQOnjQnnoRDugWC5hUuhmoQ681QR0odCHhOXSpAr9+FwQi/JaBcIu6nFX4aHLf7tl+bN5djFczTI1CZaYViSgPHSiJRABXUypJ6onR6FXZbkk0O22wyM3tWrWS/5P/uPLuPfJEzn3iYBemULPPV5x9Ti5FKlmmZoovdANKPRkmtct88YsKKCvM5SATlkuphFLmaPQl8MJ9HkcWU+5SQP6YiiB6ZVozn3VBHS7YrkIhS691tkl8xV6dp5o8dRKMYauWU+qelBAX2dEKaCbjjhJuuzVVTwuhuLo8zpVQ4qbL5jEkmkk0hn4g9qWS1WbovLxrtRQoQcN9EIHUPF7cNofqluBlBYU0NcZwh6gtEXzEH/TTpe9Og89nEC/1wGnXbIgmtHDFQp3QSOgux1WuOzGeqCrEcEzX6FPLUeQMnkfQay/pOWiDIo2/h4cemsN1//NM3ju5GLlC6wSCujrDHWbV5HCSFSH+Jt2dlQZ0ENx9HmaW6ELhbsUiuf4yyvhREUbokDhpqjYsE9lOGZWze05FJKb0pXeFBUnVePvwQunpEBeiysLo1BAX2fEkmlY5cb+tDFqDkpAd9kqzkNPpTNYiSTR521uD1106czwbFYOIKUt9nkrC+j2PIW+Eklge78HAHDGZB9dmVZkIMsFKE+hv3xmBQAK7Kh8fuP+F/FPL541/LrlQAF9nRFLZjDkk3qFUOqiOYjcfl8VlovI4+7zOFTBpBkDekr5t9p2WTZJoXMuVZxeurkbADBpckAPxlNw2CzK79RDvAcxg/1cMhmOA5PLAArtKDWJVAYvn12u2R4WBfR1RiyZxrCc50wK3RziasulQoUufOM+r1MJJs1suQC5SlRk6FSCUOiJdAaheArJNMeuYR9cdgvOLpprX4RiKfhKqHMAqn0MY+/B6cWQ0sG0mEIXE6lEAzazoYC+jkimM0hluFJaTpku5iAUumS58Ira3opZouq0xeYM6NnPzEIw628vhxPoqTCgq09g4qqx1+PE1j6P6Qo9bGBaEVB+lssrZyW7ZVNvB/yhIgFdvM8V2lOloIC+jhBer1DolOlSPo+8OYt3/tXTOV65WqEDQDJTfiAWlZeSQi9/Q65e5FguAWnNsWQakUS6opRFQO2hc+Uz2eO2Y2ufx/Rc9JCBTotA+Vkur5xZRr/XgSu39sIf0N/IFe9zPwV0olpEDrro5kcKvXyOzAQwuRRBQNXcTJwoRSpcJcpaKLd+b3Mr9JCqdF54xdXkoANqDz2t7CX0eBzY0u/G1HLU1GrNoIEB0QDK3sd4ZXIZe7b0YtDngj8U181Fz16JkeVCVIlo2N/VYYfLbqEGXQBePLWEl88sG35+QLYcRGADpI0zC4MyBaeigB6Ow2ph6HTZYbUw2CwMiXTzpZUGY0m47BZs6HIplks1fVwAdaUox4o4Obgd2NrnQSKdwexatNiPl0XIQOtcoLy0xbm1GKaWo9iztQcDPieSaY41nW6mwkMny4WoGqEkOxxWdHc4GjqGbi2arEnzpXL5n48cwVd/ftTw84UyV1sPsWQaLrtVUZqia2A5LIWkXuIWOaXUYbM05cScYCwFn8uOwU5ngUKvNEipB1woJwePA1v6pCHTZm6MGrZchEI30PHylbOSINi7rReDcgaZXqbLUigBp81iaA2VQAF9HSEsF5fNim63vaGWy31Pn8R/eODFhv1+wcxqDPOB4nnDagJyIM9R6Ck5oFdRELSUlyUi5mw2G1JAt0nWghy0sr53hQrdotoUjSTkKxUbtvZJuehm+uhGxs8Bqo1aA+/B/rPLcDusGN/QiQE5oOtluvhDcfR7nWCMlbFq49TmNEE0JSKn1mW3osftaKjlshCQAmk8lVYub+tNLJnGcjgBu5Uhk+GKOi6GUOihWK7l4rJZYFeCQPlWyZL8RRc4m1Whx2WF7pMUOudc8YUr9dAtFga7lSGRzmAtmkSP2w7GGIY7XXDaLKZmukjj50rPPFUsFwPvwStnV3DZ5m7YrJaSAX0pVHkBlhEMKXTG2C2MseOMsZOMsXs0Hv88Y+yg/N8hxliaMdZr/nKJashaLhZJoTdwalEoLq2lVFVdLZlbkzxgdXZFKYSHHoznboqqFXol2SlL4dwvuhkK/c3pNfzudw5UNUUpn2AsCZ/ThgGfE4lUBoFoCiuRBCwMZQ+HVmO3WpBMZXJaCFgsDFv63DhrUil9PJVGIpUx5qHbjb2XgVgSx+YC2LNFCndZy0U702UpHK84X98IJQM6Y8wK4D4AtwIYB/ARxti4+jmc87/inF/KOb8UwBcAPMM5N77TRNQFYbk4bVZ0N1ihh+KlizBqzYxqs82o7RKIypZLnkJ32q3KZXq5HjrnHP5gPCfzwWGtfgTavgk/fn5oztTeIorl0illSi0EY1gOJ9DtdigtJSpBnMDy89m3VJmL/sibs3j1nJQjHpZFhBH/OntyLn61dWh6DRkOXLGlR3ltl91SVKHXqqgIMKbQ9wI4yTk/zTlPAPgegNuLPP8jAL5rxuIIc8m2ec166I1q9RluAoU+q2r8NF8kd1hNVqFnA3o8lYbLbqk43XB2LYZIIo3tAx7lPmkEWnVZLsIKmV4xcVNRDugD3uzm33K4suHQauxWC5JpyUPvcWeV/tY+NyaXIhUVawHAl378Jv73o8eUtQPGArrFwuCwlh4UPStf5W3qlTZwGWM5+wtqhD3V1+CAvhHAlOr2tHxfAYwxN4BbAPxQ5/HPMMb2M8b2+/3+ctdKVInwAzscVvS47UhleM7mXj3Ra8NaT2ZzFHrpgJ5MZxCRO1SG8rNcbNZsCXuZAX1iIQQAGBv0Kvc5TJg6vyynyE2tmJf2F4wllSwXQDohL4cT6K1wQ1QggudKJJlzctjS50E8lcGcwROumpVwAiuRJF47t4p4Kq3YZKUacylrMjAoWnx+hdUCAAM+p+bnOhBLIZHO1KyoCDAW0LWuo/ROl78G4Hk9u4Vz/gDnfA/nfM/AwIDRNRImkc1ysaBb/gI2KtNFBHSzFfqjh+YMW0kzazHFTzUSMNSpisH8TVG1Qi9zU3RiPggAGBvyKfc5TZg6vyy/t2Yp9HSGI5xIw+u05XjFkk1SuX8OiBmemYI2vNvkrovH5b9ROZyW02LjqQxen1pTrgqNeOjZNRV/L+cDMXidtpyTxIDXqfm5XgrVNgcdMBbQpwFsUt0eBTCj89w7QXZL05Kbhy59ARtV/i8UbrG+F+WyEIjhru8cwL++MlX6yZA2RTf1uNHvdRjy0NXVobmFRflpi+XZAxPzIfR7HTnK1EyFPr1sjkJXz+P0Om3osFuxEIhjJZJAb5WVj3arBSvhBFIZnvN3uGJLDzwOK37+5mzZr3lGVefw0uklZd/GaA64kUwjfzCeo84B5OToq1kK17ZKFDAW0F8BMMYY28YYc0AK2g/nP4kx1gXgnQAeMneJhFmo89DFxlMjFHo6w5W1LJSRA14K8QU2usE5sxrFSLcLgz4XFgwo9ICqMVWOQk/lFhaVm50ysRDE+Sq7BTBJoZvsoQvLotMlpRUOdjoxF4jJNkl1Ct1hsyhXSWqF7rJbcfOFw/j5oTlFkBjlzGIINgvDjiEvfnVmKTt+zqhCt1tLnlQXgjHFfhIMeJ1YiyYL1P2S0selgQGdc54CcDeAxwAcBfB9zvlhxthdjLG7VE/9AIDHOeeNL/8jNIklM3BYLbBYmLLx1AiFHk6oWrCaqNBFNodeylg+s2sxDHe5MNzlMmS5iAwXm4Upag9QWS4VeOicc0wshDA26Mu532mzVpW2yDlXFKFZHnp+QBzwOnFyIYR0hpug0Jlycs+3b+64dCOCsRR+eXyhrNc87Q9jc68bV5/XjwOTK0pbASPtcwGjlkscgz5Xzn0iF30xlPvdWlT166kVho6Mc/4IgEfy7rs/7/a3AHzLrIUR5iNZA1LQ6eqQPlR6PSdqibh0ZwxFO9OVi6goNOLLRxIprEWT2NDVgXSG443ptZI/I/5Ww12ugtJ/p81aUZbLQjCOYCyFsaFche4oEUweeXMW337hrHL7kk3d+OJ7dyu3o8k04qkMujrsWA4nEI6nDG8G6hFUWS6AZC28fmQVAExR6MLGyq84vfq8PvR7HXjo4AxuuXCD4dc8sxjG9gEPrtrei2+9cBbPn1oCYFyhl9oU5ZxLCl3DcgEkC3Bjd4dyv8g6qrTNsBGo9H8dIbxeAOgWCr0BU4vC8hd3Y3dH0c505SIUuhHVL9LNhOWyFI6XLMARlstId0eOhx5PZvJ6uRgP6CfEhmieQi+Vh/7T12fw5lvSSWh6JYpvPncmJ7VPBI+LR7uU51SLuCrxuaTPzqDPpeTcV1r2LxAZQkBhxanNasH7Lx7BU8cWcmyvYmQyHGcWw9jW78HebX0AgOcmFmFhQIfBQdbOEvsYwXhKmgDWmafQvdLtfGGxGIqj223POVazoYC+jlAHdLvVAp/T1hDLReRwb+v3FO1MVy7CQ/cb8NBFDvqGrg4Md7nAeWllLzZFN3Z3KFcZ6QxHIi1ZLqJrYDkKfWJeTlnMU+hOe/FgEk6ksWPIh3/9T2/Hp6/dhlSGK61ngWzDrGxAr95Hz1foAyplWu1GnyjKArQV7O2XjiCRyuDRQ3OGXm9mLYp4KoNt/V70ehzYOeRDNCll6BjtoyLVAui/B2LfpcBDF+X/ecJiKRyvqX8OUEBfV8SSmRx10uW2N8RyEQpdDAI2Ixedc47JpTAsTDphRBPFvU9RJbqhy4Uh+QtZKhc9EEvCapE2A8VJSdgilW6KTiyE0OO2F5SDl1LokXgKHqf0XoqBJaKVAZAN6JeMdgMwR6GLxmTCg1ZbDdWmLQrVarMwTY/70k3d2NLnxkMH3zL0euLkLoq13rZdKs0XVxdGKOWhC89/IM9y6fM6wFjhhv9iqPIxfUahgL6OiKo8dEC6TG6EQhfqVuQYm5GLvhhKIJxIY3ykU75d/DWFQh/ucimbWqWyYwLRFDpdNnTKw6DjqXS24ZnNUlEvl5MLQYwN+gpUo9NePKCH4im4HcLLzpbhC0RAHxuSZnNOLZuh0PMsF5XVUK1CFyfDbrdDU0EzxnD7JSN44dSSoYyk0345oMufsbfJtks5bWud9uJpi0KI5FsudqsFvW5HoUIPkUInTERtuQCSj96InujCf95qYkAX/T5Ek6RSmS5zgSj6vQ44bVZF4RpR6J0ddiUohGKpnHYKjEnl4kY9dM45TsyHcH6e3QIADqsVqQzXndYTSaThcUjvpQgo6hOSukf5aI/bHA89loLNwhRRIMr/XXYLOhzVdcwUCr3Y5urNFw6Dc+DF00slX+/MYhheuYkYkFXoRjdEAQOWi/wZy98UBSTVnv+5zm/AVgsooK8jCgO6A2uNUOiK5SIFMqNphsUQHfmu3Cp9cUudJGZWY8qw7F63A3YrKx3Qo0l0ulQBPZ4b0AEp/c6oh+4PxbEWTWLHoEZAL5ExE0mk4M6zPtSWy5LcFtjntGFTTwemTPLQva6sBy2842rL/oHs8RbbXB3uND468ZQ/hG39HmWt/V4ndg37yrI8Stle84E4OuxWTdWfX/6fTGewGknWtKgIoH7o6wqRLy3oaZBCFx76cJeraGe6cphcCsNqYbh8SzeA0gF9di2qDFCwWKSGSqVy0QOxFDo7bIrKC8ZSsMgBQ/xdjfT/EJxUNkR9BY85VQFdS/2G41mFbrda0O915FkucfTI9sVojxuvnls1tKZiSH1csiGj1+2AzcLQa4LqFHZVsYAu2vMaCehnFsO4fHNPzn3/8Ik9ZWWYSBvTRTz0YBxDndrDKgZ8TsX2AbJXTP0+UuiEScRS6ZxN0e4OOwKxpKlDeI0QjKfgsEm9T7QuTSvhzGIYoz0dGPS5YGGlN1pnV2PKsGxALtcu6aFLCl0EtVA8hZj8hXfKf1eHzbjlotWUSyAUq1ZAEZW26rzyoU5X3qZotsnVaE8H1qJJwyl/egRjKfhUwyEsFoZ+r7PqlEVApdCLKGibnJm1Gi1+VRlLpvHWajSneyUgdUQc7nLp/FQhpdIWFwKxgqIigei4KFJyxZ5OrRU6BfR1RDRRaLlwntujpB6EVXMdB7zafS/KZXIpgi19HlgtDH06zZEEwVgSwXgKG1RFH0M+lzEP3WVXglpQ7aHbsgHdqEKfWAii02UryJIQrwNob7CKtgliKDUgBfRcDz2u+LWitWu1PV2CGgOW79y7Cb928UhVrwtkB0WXKlDqctuxVkKhTy5FwHl2071SSnvocQx0agfoAZ9TmcAEZOsCalklClBAb0m++dwZ/MuvJsv+uXwPXaSa1TvTJRTLBnS93tHlwDnH2aUwtspDhQd12pcKhJJVK3Qj5f+BaK7lEoonlSwIYbnYrRbEjSr0+RB2DBVmuACqIcUaAUVYVm5n9r0c6nQWZLmIcvzRHunEVa2PLoZbqPmDG3fgN67cpPMTxnFYpWMppfa7Okqn2p5ZlK58zhsovPIpB6/LhnSGK9k9+SwEYhjSUej5o+iWwqLTIil0Io9/fWUKP3nNWD6umlgqU6DQgQYE9HjWLhjwOavu57ISSSIYS2GL7ImXsnFmlCrRrEIf7HQiGEshouozoyaRyiCaTOduisYKN0XLmTQ0sRAqKCgSOItsioqAnq/QF0MJ5flL4QR65WrgTT2yQq8y00X0Qq8FdptQ6MUDupHRiaJt7tYqFfr58glBWGNqQvEUwol0QVGRYDAvoC8GSaETOvhDcWUmp1HSGY5EKndTVOz4L9e5/D8UT8Irq8sBnxOrkcLOdOUgerioFXqxgD67KgW2YVX+sFBaej66UGmdHVkPPajy0EVAdxr00KMJaUC1sEPyKVakJIZsuB1qhS6Xm4ekFgbBWEpR6N1uOzwOa9W56CENy8UsjGyKAkB3R+nRiaf9YQz6nGXlnGuxc1jarD4xV9iLXakS1bDLgKxCn5T/5ovhOBw2S9VrKgUF9BYjKc9dDJc5aUgETPWmaK8S0Os7NSgcT6ssF+3OdOVwNk+RDficWAzFdceWza7FwBhyNsiUaksd20VUSXZ22OC0SWX+kkIvtFyMKHRhG3R3aAew7NT5whOdeO/VwWG4M5tLL7oKiuwTxhg29ebmopc70o1zLqUt1iggGdkUBYxVN4umXNWysbsDbodVc7iGXlGRYGufB2ODXjyw7zSS6Yw0S9SjXTRlJhTQWwyxuVLu6DhRCq+2XMSOu2izWi9Cqs5/+V5jJZxdisDCsl7xgNeJVIbrWkmza1EMeJ05KWylyv/FxrHoBe512qraFBUZJyIVLx9DCl0VXMWl//xaTDVIIRscR3s6ML0izeb828eP4+I/exzHNZSnHtFkGukMr5nl4s37POjR3VF8Fi7nHKf9IWzrr84/B6QsnrFBr9JATY3W6Dk1VgvDPbfuwpnFML778jksheI1988BykNvOUTgKzegx1K5ShKQJhe57BZlEEK9UF+6iy+wkXJuPSaXwhjp7lBU7aDKftD6Es2uxXIyXAB1taWeQs9aLoC0YRaKZxW6U5WHbuS9UU4QHdpfwWIeekjx0Astl/lATFmj2r4Y7XHjxVNL+Mw/H8CTR+cBSB0bdw7vLLlWoLAxl9m896IN6PM6c9rNatEtz8KNJNKa7YAXQ9IcUa1U0ErYMeTDL08Uzj/OWi76aZDX7xrEVdt7ce+TE+h227FZx14zE1LoLYY/JH2QEqlMWV398jfvBH0eZ06XvnoQiqWUDT3xhahmY/TsUkQpEgLUJwntyeunFkIYzQscXqcNbodVt5+LGG4hFLXPaddW6AYtF+UEoaN4i6Utio1btUJXql3loc1A7uzK0Z4OhBNpPH18AV/+tXG8bVsvnjgyX3KdgloHdJfdinfuKD1nWCku0rFdhJoW/ne17BjyKYOw1SwE43DaLLonZECyur743t1YDidw2h+ueR8XgAJ6y6G2Jsrx0bUsF0Dy0fM/rLVEFMWI1D8RdKqxXCaXwtjSl1U/oseI1mu+em4VM2sxXL9rMOd+xpicy11CobvUCj2JWCqtTIECALvNYqjb4lo0V/HnU2z6kRh2rFbootp1fi2mvJ/qjJHrdgzgkk3d+Off2YvfumYbbhofwvH5IM4tGdsozTbmauxFvRjMorcxqvSX18keKpcdYmM0z3ZZCEij50p54hePduO2S6Q8/Vr3cQEooLcc6iBVju0STzVHQBfj54Rnarda0OtxlF1cNB+I4dFDc/jLR49hNZLMKSLR60cNAA8ffAtOmwXvuWCo4LGhTmdpD11WZD7ZQ48nM4rdAgBOowpdVvydOgFSVJ5qvZai0B25PzvU6cR8UPLQGYMyCByQlOZDn70GV5/XDwB4z/gwAODxI8b6i2cVem08dKOIwSx6xUUn5oPocduVk3q17JTbMkzkBXSt0XN6fP7mnfA4rErnx1pCHnqLkaPQdXKmtRBeb/60ll6PAyc18mxrhWidq86WKJVmmM/EfBA337sPGS5tPl0w0plzue5x2uBxWAssl2Q6g5+9MYsbdw9pBqYBnwuH39IeRReIJWGzMOXv53XZEPKnCoq1DG+KGlToWumc4YR0VSBsGcFQpwsn5oNYCSfQ1WGHrUjfks19buwa9uGJI/P41LXbS65XiIdGK3QR0PUsl+NzQd1irUoY6nTC57IVZLosBGOGbZ1NvW689MUbap6yCFBA1+SBfaeQTHN89t3nN3opBajT+0KxSiyX3C95r6e+PdGVohjVh7vcfi4vnFpChgPf/u29eNu23oKrDuU18xT68ycXsRRO4PZLtUvVe9123YwfqUrUrgQKr9OGUCyFeF5uv9FeLoFYEm6HVbdZVFEPPZ7KqRIVDHW68NzEolwlWvry/qbxIdz39EmshBMl0wWF5VKPoFSMbsVyKQzonHNMzIdwx2UbTft9jDHsHPLhxFyu6FkIxnHtWGnPX1CvKxuyXDR4+PUZPHxwptHL0MQvb8YA5VkuMY08dEAK6JFEWtncqzVi0o+6L/VAid4r+RyYXMFQpxPXjfVrBnNAtBTItU8eOjiDTpcN79yp/UXs8TiwFk0ipRGQpT4u2TX7XHZ5pmRa2RAFjOehS8My9L/kxUr/Q/F0TpWoYKjThWA8hemViKE2sTeNDyHDgaeOLZR8btNZLhoKfXYthmA8pfjeZrFj2IcTC0ElVTKaSCMYS5VMsWwEFNA1WAwmqi5HrxX+UFzZAAyXUS2qtykqvvj1ykXXKooZ6HTmdKYrxavnVnDFlp6il9X5/aijiTQeOzyH9128QUlvzEeoWq3L+UA0mWOP+Fw2JFJSRWaB5WJwU7RYhkSxTdFIIpVTJSoQufTH54OGFPpFG7sw3OnCEwZ89ICGVdYIxKg/rY6LwhbZqdGOuBp2DHqxGkkqokP0zNErKmokFNDzyGQ4FkNSmlI509vrhT8YVzYAxRR2I4g8dGee5SIuteuVi67lofd5HEikM4auOBYCMUyvRAt6XeeTb+M8cXQekUQat12ifzku8rZXNE5ugViuohbrXwzFcy0XqwXJNC95cgrEkrpFRYCUteKwap8cwol0TsqiQFSLxpIZQwGdMYYbxwex78RiySs00VDNaqltpaMRuju0Oy6KjcsdJmW4CITiFyeMUkVFjYQCeh5r0SRScll0PbM/jBBJpBCKp5QS93L6uYgS8nzLRennUicfPaSh0LOBtPQJ6tVzKwCAy7eUDujqPPGHXnsLw50uvG1br+7PZFshaAT0PEUt1u8PxgsUOlB6ULRoxVsMh017pmUknlJ64ahRz/g0EtAB4KbxYUSTabxUYqxbMJZsuDoXdLvtmh768bkQBn1OpemcWQjFf0IeSPLcxCIAYKSbFHrTox4ubMbgBTMRHdu2yUU0ZuWhA/Xr56IV0HvLOKkcmFyBw2rBBfIwaD3ULQX2nfDjqWML+NAVo0q+uBY9RbpP5gdgke2xHEnkWDjFrJKc15M3WYsh2TfaWS75KYtA1nIBoDTmKsXlm7sBAId0snsEWq1zG0V3h0PTcjkxL2W4mE2f14k+jwMn5oJ48sg8/u4XE3j/xRuqbs9bCyig56EO4s0W0EWV6HCXC06DJeaCWCoNq4UVZFUo/VzqZLloZbkI20fL6sjn1XOruGi0S9cHF4iAfnwuiD/+t9dx/qAXd19fPGupt0j3yfwALDZ1OUdBlgtQOqCvRZO6OegCp04KZCSRyikqEvhcduV+o7MzfS47tvS5cXgmUPR5tey0WC5dGgo9k+GYWKhNQAekPP7nTy3ic997DReOdOGvPnRJzRttVQIF9Dz8TazQxXoGfFJubFkBPZkpsFsAqVDGamF1s5fU4+cEfUWsDjXxVBpvTq/hihJ2C5D1N7/44zexEknga3deqpsRI9Ab+JHtha7KclGNYtOyXJJpfQ89Iw9NMKLQ9QZcaHnoQHajzqjlAgAXjHSWDOjBWBLeBme4CLo77AVTtqZWIoglM9g5XBvVvHPYh+mVKNxOG/7hE3s057w2AxTQ88hR6E2W6aIO6B6nrTzLJZkuyEEHpI2xHnf9ctHV4+cEikIvsYbDMwEk0hnFJiiG0s8lGMcfv2cnLhjpKvkzTps0wT3/xJLfmAvITbtU/13tBiyXcCKFDNfvtCjQ6wujHhCdTyUBfXxDJ84tR4rOHG0my6Wro3DIhegcqTVw2wz2bO2B22HFAx+/oqy5pPWmOd6hJmIxlIDdyuC0WZtPoYcSsDDJJvE4bGUVFsWSaV2bos/jqJvlEtLoqe1z2mAzcJXw6qS8IVoiwwWQ/kZOmwWXb+7Bpw1UQgp6PPYC60fdOldZszqg27Q2RfU3rJXe6iUUr9NeGNC1BkSrET56eQpdOtkdnQngbdv7dNdcyiKqF91uOyKJNOKp7Gda6eFiUpfFfN5/8QhuGh8qafU1GlLoefiDcfR7nWWXo9cDfzCOXo8TVgtT2rcaJZZM614m1rOfi3r8nIAxhh6NNRyfC+L2+57Hi6ekDIwDkysY7enIyebQw2ph+MFdV+OBT1xRdCM0n163oyAnXz3cQqA+KeVYLlb9giCBSLkrlocuXiv/dbQGRKsZ6qrMcgGga7tIwy2aJ8ulS968VhcXnZgPYWN3R00Ln5o9mAOk0AtYDMUx4HPCZW9ChR6MK1aC12nLGQpcilgyo2m5ANJkm6MlPFSzCMWT8GkEhl53YUB/4dQiXp9axce+8Sv86ft249VzK7hKR0FqcdFoaZslH60Ti5ZCF1OLkmme83d1GvDQS7XOzb6WtUChRzQGRKv56N4tOK/fW3K/QM2Az4l+r0M3oL+1GkU8lcHmvto3lzKCaDq2FkkqDbKkDJfmyzqpN6TQ81gMSQpdjDEzwmOH53D1XzxV8/J5fyiuDJn1OMu3XLQ2RQE5mKr867m1GC7788fx+X97HUsm7yOE42l4NIJRj8de4KEvBOOwWRjevXMQX/7pEcwH4obslmrQOrFoeehiahGQq9CNeOilGnMJpE3R3M9UOFFcoW/uc+M3rtxU9HXzYYxhfKQLR2a1A/oROdCPbyieKlov8ht0JdMZnPKHapbh0koYCuiMsVsYY8cZYycZY/foPOddjLGDjLHDjLFnzF1m/fAH4xjwOsvqL/Ka3GP7rdXqpqqXYjFPoZdTWBTN6wqoptfjwGok28Pk5bPLWIkk8YNXp3H93zyD77w0WfYMSj1C8ZRmtoSW7bMQiGPQ58QDH78Cv3/DGHxOG64d6zdlHXr0eBwaHrq25y02Rp1ahUXFAnosd1iGHlpZLmIjXKv0vxouGOnExHxQs7vj4ZkAGAN2b2iOgCkadAnr6rQ/jGSaY1eTrK+RlAzojDErgPsA3ApgHMBHGGPjec/pBvB/AdzGOb8AwIfNX2rtyWQ4lsIJ9PscUqVhPKUU5BRD9NCeXa18jFopOOd5lou1rCyXWDKjvynqFVkm0hfkyEwAdivDv//etdi9wYf/+pNDePh1c5qVhXSqHKVMm9zMhYVgDAOdLlgsDH900w688eX3YHuNizl6PQ6E85qVZRV6/mauFJBdNo089GKbohoWjhZOjb4wWnn8ZjC+oROpjNStMJ/DMwFs7/doFjM1gvypRUflK4vdTXIF0UiMKPS9AE5yzk9zzhMAvgfg9rzn/EcAP+KcnwMAznnp9m1NyEokgXSGSwpdmUZfWqXPrUmBfGatdgo9EE0hkc4ojfs9ThuiybRmZ0At4iU2RYFsHvjhmTXsGPJhfKQT3/30VfC5bHjl7LIJR6Gd5SLWsCr//QVCoQvqUcghqkXVhStr0dxe6AKh0HMtF2mNiZT+FY3YzPOWyBrRKv0XA6LNDuhiY/SIho9+ZGYN4wbSPutFl7BcZIvu6GwADqulKSs3642RgL4RwJTq9rR8n5odAHoYY79kjB1gjH3CrAXWE9FrvN/nzMljLkU9FLqoElVbLkDWUy1FNJnOUZJqet3ZgM45x5GZgPIFZ4xhfEPpwhMjFEu563E7kOHIKRhZCMZyytnrQa9cXKS2f2ZWoxjuchWcUHwaHrrTQC+XQEzaGC7V6EpToScKB0SbwdY+DzwOKw7P5LYAWAknMLMWK9lqoZ74nDZYWPbEeGQ2gPMHvbq95dcTRv4CWp+6fPlhA3AFgPcBuBnAnzLGdhS8EGOfYYztZ4zt9/sLJ2k3GuGZ98seuvq+YoiAPheonUJfUBUVAaqAbtB2yZ+so6bXmw3oC8E4lsKJnA2wC0a6cGwukKOeKyF//FzOGvL6ucRTaayoshjqhVY/l8mlSM7MUkFWoau7LeqPjhMY6eMC6GW5SCdwvUrRSrFYGHZrnLjFRmkzBXSLhUnFRRFhuQTJbpExEtCnAai3zUcB5Buq0wAe5ZyHOeeLAPYBuCT/hTjnD3DO93DO9wwMGJ/2US+EvTLgcyqX+qWqRYOxpKKSZypQ6IlUBr/zrVfw/MnFEmuTAoxYl1C5RnPRo4Ysl7ii0C7YmL3EvmCkE7FkBmcWqxtVp9U6N38NYkPS36AWpX3ewjYE55Yj2NxbmLLn07BcsqX/xRW6kYCuneVSG4UOAOMjnTg6G8jZABefh2bJcBF0ux1YjUo9yhdD8abZsG00RgL6KwDGGGPbGGMOAHcCeDjvOQ8BuJYxZmOMuQG8DcBRc5dae0RA7/c60etxgLHSCl2ocwsDZivw0PefXcZTxxbw+X97vajaVsr+vZJiFerQSEDnnEt56DqWi1ClS+EEDr9VuMF0wcbihSdGKbahl+/jiyuSeg8RyFfogVgSy+GEtkJXNkW1PPQihUUGGnMB2dJ/dW/1bJaL+RuUF4x0IpxIY3I5otx3ZCaA4U4X+kwaumwWXR12rEWTyoZos51wGkXJgM45TwG4G8BjkIL09znnhxljdzHG7pKfcxTAowDeAPAygAc554dqt+za4A/G4bBZ0OmywWa1oNftKBnQ59akx3cOd1bkoe+bWITVwjCzFsPXnpoovjarRcm0KMdyEalvLh1VZ7dKx7wSTuDwTABb+9w5Kvq8AS8cNkvVAV1r/JygJz+gB3ItpnrR1WEHY9nuk+eWpOC2pbcwoPu0LBcjaYtRYwrdabMgw6H05wf0B0SbgWgBIFosANJJvJnsFkFXhx1rkQRluORh6FPBOX+Ec76Dc34e5/x/yvfdzzm/X/Wcv+Kcj3POL+Sc31uj9dYUf0jKQRebX0aKi+ZkhX7Z5m4E4yllmK5R9p3wY8+WHtx55SZ847kzyge0YG1yyqJYmygsMVJcJFLwXEVKl/u8TiyFEzgyGyhoZGW3WrBzyKeZAVEOWuPnBMrGbEQodOnvOljnTVGb1YKujmyR06QI6BpVklqFRUYGXARjqZI56DmvpTo56A2INoPdGzqxvd+DB587g0yGI5pI45Q/1JQBvdstNeg6NhfEcKer5JDr9QJtC6vwB+PoVylCI9PoheVy6aZuANkURqO/78hsANftGMCf3LILXR12fOnHb2oW8fhDuWvzlWG5xOTUt2Ll4L0eByaXIji3HMG4xhdYarG6ZnjupxbFPPQOhxUuu0Xx0BcCcVgtTOnXXk/U1aKTy2EAUgVmPteO9eNDV4xig6r7npEBF4Fo6WlFgCpjRvVa4YT2gGgzsFoY7r7+fBydDeDJo/M4NhdAhkPz89BouuVN0aOzAfLPVVBAV7EYSmDAmz3TG6kWnVuLoavDrsz5nCkjoIuN0OvGBtDjceCL792NV8+t4ss/PZxjpZxcCOL4XCBnbZ4yLBeh0Dsc+m93r8eBQ2IDTOMLPD7SiZVIErNlHF8+WtOKctbgdijDJRaCMfR7HQ2ZYdnjybYTPrcUQb/Xobnm7QNe/PWHL4FNlS7HGIPdynQVejrDEYynSjbmAgCHfEWlrhbVGxBtFrddMoKtfW587akJxWIz0nq43nS5HQjEkji5ECK7RUVzlH41Cf5gHJeoGjoN+Jzwh6Rp9HpFLXOBGIY7XYpKmy2j/H/fCT963HblkvbXL9+I16dW8U8vTuKJI/P4wnt348hMAA8+exoepw2fePtW5WdFPxQjCj1qwHLpdTsgxLfWJba68GSku8PQ8eVTKqCrA+l8IF73lEVlHW6H0sZhcimCzRr+eTH0+pgDUCw5Iwpdy3IJx7UHRJuFzWrBZ999Pj7/gzfwjefOwOeyYbSnsve7lnR32ME5kOKcAroKUugy6QzHcjieswk34HMikcoovTe0mA/EMNTlwlCnC4wZV+icc+ybWMQ7xgaU9q6MMXzljgvxw999O7rdDvz+d1/D/c+cwgcu24hf/Od34rod2VRPp80Ku5UZ6ueieOjFLBdZ/Uspm4WBdNdwJxirLtOlVNm6up/LQjBe96Ki7DqyPdEnl8Ka/nkxHDqj4wBVXxiDm6JAbhuBcFx7/JyZ3HHZRmzq7cCZxTDGN3Q25ag19R4EBfQspNBlViIJZLiUsihQDxrW28SaW4th17APdqsFgz4n5oqkLkYT2Vzwo7NBLIbiuE6j2dQVW3rx07uvwU8OzmBbv0d35JrX4NSiqIGALsbA6aV/eZw2bOvzFFQSapHOcGQ4L6jc0xo/p6bH7cA5OWXOH4wp+xL1pscjdZ+MJdOYDcTKV+g2i24euugLU86maCyZ66HXegPQbrXg7nefjz/54ZtNabcA2Y6LTptFsTsJUugK/mBhmlypatFUOoPFUBzDcq70cFeHrsf8ytllXPTlx/Dlhw8jmc7g2QmpUvbaMe0CK5vVgg9dMVp0fqbHaWzIRVzZFC3uoQPFKwLHRzqVysFMhuMHB6ZxcGo15zkHp1ZxzVd/gT/5wRsFP681fi5/DcvhBJLpDBZDiboXFSnrcDuQSGVwYj4IzoGt/eUFdHsRyyXbmMuIh16YMaM3INpsPnj5KD6ydxM+eHl+l4/mQAT0ncO+huyzNCuk0GXURUWCgRLVov5QHBmenRIz0uVSRmHl8/89cQJWC8O3XjiL43NBxFJp7BjyVjWf0GswoGc3RYtnuQDFN8AuGOnCz96YxcxqFH/+0yN49PAcAODOKzfhv9yyC784toAv/vhNJFIZPHZ4Dsl0JkelB6IpzV7o6jUEYyklU6jeRUXqdQBQTlZaVaLFcNgsiOso9DWDvdABnSyXGnvoArvVgr/44MU1/z2V0iW30N09THaLGgroMtk+LtnLWRHcF3UUugg8QqFv6OrAMyf8BZuor5xdxgunlvCn7x9HV4ddCXqfese2qtZctuVSZFP0qu19+IMbx3D9rkHd5wj1ftvXn8NyOIF7bt2F5XAC33juDH76+gzCiTSuPq8Pt10ygnt+9CZenVxRZlRyzqWrlI36JwxhJRyTB/42TKGLgH5uFQA0q0SLUWxTVGtYhh4ioOdnudRDoTc7Az4nHDZL0SvY9QgFdBl1HxdBV4cddivTVegiB10oyZFuFyKJNAJ5hSN/99QE+r1O/Me9m9HhsGJs0Iv//dgxfHhPeZNl8vE4bQVTfrQwkofuslvxBzcW9FPLQaQzJtMc3/7tvYpd9OuXj+KrPz+KHUM+fP7mnYgk0/jSTw7h2YlFJaAfeiuA2bUY/ugm/d8hiouOybZOvYuKBOLE8trUKjwOq7K/YJSiHnrU2HALIDvDUpwcMhmOSCLdNH3JG0lXhx2//ON3KWKKkKBPhow/GIfTZsnxeC0Whv4iueiKQu/KKnRA6ukivrAHJlfw7MQivvTe3YrlccmmbvzLp66qes1epw1TK5GSzxMKXW8EnVH6vU78429difMHvdik2ijcOezDP35yr3K702rBZZu68eyEH398804AwBNH5mBhwA27h3Rfv0duXXtsXij0Blku8onlzGIYuyvI8iil0C3MWHMth6LQpfcvIr+PzTKsudFUmj7bztAnQ2YxlMgprRcUqxadC8RhtzIlAAwruegx7JK9va89NYE+jwMfvWqz6WsuZrlMzAfxD8+exhvTa5hYCMFmYXAVKSwyyruLWDJqrh0bwL1PncBKOIEejwOPH5nHni29RafRi8eOzwXBWK79VU/UWSRaPVxKUTxtUerjYuQkkV91WmpANEFQlouMGA6dz4DXqTvkYj4Qw6DPpeSRj3RLAV1MLnrt3Ar2nfDj09dtr8llcrFB0fc+OYGHDs5gqNOF333nefjOp96mO4KuFly7ox+cA8+dXMTUcgTH5oK4aVxfnQO5yrjP48ypwKwnna7s8IktZWa4AMUtlzWDZf8A4LTnBvRSA6IJgj4ZkLzJkwshXLa5u+CxLX0ePH9qEekML0iPmluL5WSpDPpcsFqY0nXx756aQI/bjo9ftaUm6/Y6rQgn0shkuHJSAaRe3Psm/Ljj0o34yw81JlPh4o1d6HTZ8OyEXzkhlgro3XJAT2d4wzZEAanAq8ftwGIoji1lZrgAUoZI/nBnQSBmrOwfyCp08Vq1GhBNtA+k0AG8eHoJs2sx3HLhhoLHdm/wycMdwgWPzctl/wKrhWHI58TsWgyvT63i6eN+fOra7abPfxSINrSRZG616IHJFQRjKbx7V+OGiNisFrxjrB/PTiziiSNz2DHkxdYSBSAOm0UZ69aoKlGBGEVXboYLIFsuupuiSUMbouJ1AJXlUqN5okT7QAEdwPf3T6HTZcN7NBSkKCs+NldY8j4fiBXkSg93uTC7FsXfPTWBrg47PvH22qhzQDW1KM92efr4AuxWhnfoFC3Vi2vHBjC7FsNLp5dLqnOB8K8btSGqrEO+Wii3ShQAnCU2RQ1bLiLLJZ2r0CmgE3qs+4C+Fk3i0UNzuP3SjZppfWNDXtgsrKBPuRg9N9yVqyQ3dHfg9alVPHVsAZ96xzb4DH55K0FkO+QXFz19bAF7t/U2PBviWlVbg5vGhw39jBLQG67QHbBbWUWZFGZ56GL6UVy+Aqvl+DmiPVj3p/qfvTGDeCqDD+8Z1XzcabPivAEvjs7mVoDm56ALRrpcCCfS6HTZ8JvXbK3JmgVaU4umVyI4MR/Cb1SZ424Goz1ubB/wIBRL4eIiBUVq+pSA3liFfs35/XDaLBWVlRcv/TfuoTPG4FRVndZqQDTRPrTVJ6NYm1s9vr9/GjuHfEUrGHdv8OFXZ5Zz7hOj5/ILG0Qu+m+/Y5thJVYpWoOinz4u9Ygxml5Ya/7XBy5CMp3J2bQthrA6GrkpCgAfu2oLPlbhZrZe2mIilUE0mTbsoee/Fil0ohRtY7nMrkUx/t8ew4HJ5dJPljkxH8TrU6v48J7RoieC3Rs6MbsWw6qqKlOMnsvvxXLdjn7ccsEwPnlNdWX9RtCyXJ4+toDNvW5sb5IOdFdt79NtQKaF2IxsdECvBr1NUdFJspyA7rRlM2bEpihVihJ6tE1APzITQDSZxi9lhWqEf9s/BZuF4QOXFe8oJzZGj6h8dDHIIt9yOX/Qh/s/fkVZX9pKybdcYsk0Xji1iOt3DTZlD2sj9DSJ5VINdqsFyTQvGNd375Mn4LJbDO8nAJLll1ClLdqtrCYDoon2oG1O9dMrUoDNb+eqx2l/CN97eQo37B5En0ZBkRoR0I/OBnH1edJG3y9P+LFjyFu0P0qtybdcXjy9hFgyg3ftbGx2SzW8/6IRxJMZjFTRhbLROFVtb0WmysGpVfzsjVn83vXnl9Vh06FS6OF4ijJciKK0zal+Sr6cPTi1qjlkWc1aNIlPfXs/7DYL/uv7xku+9oDPiX6vU2kaNbUcwYHJFdx+aWN7RedbLo8fnoPLbsFVckOsVmRznxt/eNOOlr3CAApL9jnn+F+PHEW/14H/9M7zyn6tqOyd13JANNEetE1AFwo9GEvhtEYRkCCVzuD3vvsazi1H8PcfvTynyVQxdm/w4aici/7w6zMApIG6jcRlt8DCJOU2H4jhh6++hdsv0U6/JOpHfkHQk0cX8PKZZXzuxh1lp5IO+Jx48ugCbr/vebw+tUpVokRR2iagT61ElGG2xWyXv3z0GPad8OMrd1yotHY1wviGTpyYDyGVzuAnr72FPVt6DJ8MagVjTBpyEUvh/3/mNNIZjs+++/yGromAMtQjmeZIpTP46s+PYvuAB3deWX4q6X0fvRz//dfGEY6nMLEQUib1EIQWbXP9Nr0Sxfsu3oCHD87g4NQKPnRFYV55PJXGN58/i1+/fBQf2Vte98NdG3xIpDL49zdnMbEQwlduv8CspVeF12nD2aUIXjq9hA9cthGbKyhVJ8xFrdC/9cJZnPKH8Q+f2FMwY9UIXR12fPKabfitq7fi1XOrFNCJorRFQA/EkliLJrGl142LR7t0FfqZxTDSGY53VrBpKDZG/+bxE7BZGN53cWPtFoHHacMzJ/ywMOBuUudNgQjoZ5fC+NsnTuCGXYO4cXd1dQGMMZrOQ5SkLSyX6WXJP9/U68Zlm7txbDaozNFUMzEfAgCMDXrL/h3nDXjhsFpwbjmCa8f6i/b1rieiQdcdl24s2fyKqA9iU/TPfnoYGc7x5dsuaOlNXqJ1aI+ALk/tGe3pwKWbepDKcBx6a63geRMLIVgYsK2CwGe3WnC+fCK4o0Teej3xOm2wMOCz15M6bxYcNil4n/KH8bkbdjR8r4VYP7SF5TIlZ7hs6nErpfcHp1axZ2tvzvMm5oPY0uepOAvkoo1dOLsUxo1FxqjVmw9dMYprx/px3kD5Vx1EbXBYpc/XjiEvPnVt7SuGCULQFgF9eiUCj8OKbrc02mtjdwde0/DRJxZCisquhM/fshO/dc3WpiruaHQuPFHIlj43Rrpc+IsPXlTRRihBVErzRKYqmFqOYrTHrfiUl27uxsFzqznPSaQyOLsYxs0XVK6u+71OzTF1BKFmU68bL3zhhkYvg1iHtIV8mF6JYFNvtm/1ZZu68dZqFAvBmHLf5FIYqQzH2KCvEUskCIKoOS0f0DnnmF6RFLrg0k3dAIDXVCp9YkHKcKnGciEIgmhmDAV0xtgtjLHjjLGTjLF7NB5/F2NsjTF2UP7vv5m/VG3WokmE4imlShQALtzYBbfDin0nsp0XT8wHwRho85AgiLalZEBnjFkB3AfgVgDjAD7CGNPqaPUs5/xS+b8/N3mdukzJOehqhe6yW3Hd2ACePDqvNOqaWAhhU48bHdQLgyCINsWIQt8L4CTn/DTnPAHgewBur+2yjCNy0NUeOgDcND6E+UAcb8r56CfnQxUVFBEEQbQKRgL6RgBTqtvT8n35vJ0x9jpj7OeMMc1GJ4yxzzDG9jPG9vv9xgdRFGNKKSrKLd64ftcgrBaGJ47MI5XO4PRiCGNDtCFKEET7YiSga9Us5zccfxXAFs75JQD+D4CfaL0Q5/wBzvkezvmegQFzhjBMr0Thc9kKJgT1eBy4cmsPnjgyj7NLESTTnBQ6QRBtjZGAPg1A3fdzFMCM+gmc8wDnPCT/+xEAdsZYv2mrLMLUcgSberRLq28aH8bx+SCeOjoPABgbooBOEET7YiSgvwJgjDG2jTHmAHAngIfVT2CMDTO5qocxtld+3SWzF6uFlLLYofnYe8alIqIHnzsDgDJcCIJob0pWinLOU4yxuwE8BsAK4Juc88OMsbvkx+8H8CEAv8sYSwGIAriT50/IrQEiB/26Hdr2zaZeN3YN+3BsLoiN3R1NVbJPEARhNoYinGyjPJJ33/2qf38dwNfNXVpplsIJRJNpXYUOSNkux+aC2EF2C0EQbU7LSdZnTvjxP352BIA0VR2ArocOAO8ZH8b/+cVJynAhCKLtabmA7nXacjY3r9zai73be3Wff+HGTnzuhjG8/+IN9VgeQRBEw2B1sLo12bNnD9+/f39DfjdBEESrwhg7wDnfo/VYyzfnIgiCICQooBMEQbQJFNAJgiDaBAroBEEQbQIFdIIgiDaBAjpBEESbQAGdIAiiTaCAThAE0SY0rLCIMeYHMFnhj/cDWDRxOa3Cejzu9XjMwPo87vV4zED5x72Fc67ZkbBhAb0aGGP79Sql2pn1eNzr8ZiB9Xnc6/GYAXOPmywXgiCINoECOkEQRJvQqgH9gUYvoEGsx+Nej8cMrM/jXo/HDJh43C3poRMEQRCFtKpCJwiCIPKggE4QBNEmtFxAZ4zdwhg7zhg7yRi7p9HrqQWMsU2MsacZY0cZY4cZY5+T7+9ljD3BGJuQ/9/T6LWaDWPMyhh7jTH2M/n2ejjmbsbYDxhjx+T3/O3r5Lj/UP58H2KMfZcx5mq342aMfZMxtsAYO6S6T/cYGWNfkGPbccbYzeX+vpYK6IwxK4D7ANwKYBzARxhj441dVU1IAfjPnPPdAK4C8Fn5OO8B8BTnfAzAU/LtduNzAI6qbq+HY/4agEc557sAXALp+Nv6uBljGwH8PoA9nPMLAVgB3In2O+5vAbgl7z7NY5S/43cCuED+mf8rxzzDtFRAB7AXwEnO+WnOeQLA9wDc3uA1mQ7nfJZz/qr87yCkL/hGSMf6bflp3wZwR0MWWCMYY6MA3gfgQdXd7X7MnQCuA/ANAOCcJzjnq2jz45axAehgjNkAuAHMoM2Om3O+D8By3t16x3g7gO9xzuOc8zMATkKKeYZptYC+EcCU6va0fF/bwhjbCuAyAL8CMMQ5nwWkoA9gsIFLqwX3AvgvADKq+9r9mLcD8AP4R9lqepAx5kGbHzfn/C0Afw3gHIBZAGuc88fR5scto3eMVce3VgvoTOO+ts27ZIx5AfwQwB9wzgONXk8tYYy9H8AC5/xAo9dSZ2wALgfw95zzywCE0fo2Q0lk3/h2ANsAjADwMMY+1thVNZyq41urBfRpAJtUt0chXaa1HYwxO6Rg/i+c8x/Jd88zxjbIj28AsNCo9dWAawDcxhg7C8lKu54x9h209zED0md6mnP+K/n2DyAF+HY/7hsBnOGc+znnSQA/AnA12v+4Af1jrDq+tVpAfwXAGGNsG2PMAWkD4eEGr8l0GGMMkqd6lHP+t6qHHgbwm/K/fxPAQ/VeW63gnH+Bcz7KOd8K6X39Bef8Y2jjYwYAzvkcgCnG2E75rhsAHEGbHzckq+Uqxphb/rzfAGmvqN2PG9A/xocB3MkYczLGtgEYA/ByWa/MOW+p/wC8F8AJAKcAfKnR66nRMb4D0qXWGwAOyv+9F0AfpF3xCfn/vY1ea42O/10Afib/u+2PGcClAPbL7/dPAPSsk+P+MwDHABwC8M8AnO123AC+C2mPIAlJgf9OsWME8CU5th0HcGu5v49K/wmCINqEVrNcCIIgCB0ooBMEQbQJFNAJgiDaBAroBEEQbQIFdIIgiDaBAjpBEESbQAGdIAiiTfh/YwD/PHPUFfAAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(history.epoch,history.history.get('acc'))"
   ]
  },
  {
   "source": [
    "# 对于模型的理解\n",
    "\n",
    "## 线性回归\n",
    "## 多元线性回归\n",
    "\n",
    "## 二分类\n",
    "```py\n",
    "# 模型构建\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(4,input_shape=(3,),activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(4,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
    "model.summary()\n",
    "\n",
    "# 定义优化器、损失、评估方法\n",
    "model.compile(optimizer='adam',\n",
    "    loss = 'binary_crossentropy',\n",
    "    metrics = 'acc')\n",
    "```\n",
    "\n",
    "## 多分类\n",
    "* 多分类有两种编码方式。一种是使用单个输出的编码。一种是使用onehot编码。\n",
    "\n",
    "### 单输出编码的多分类\n",
    "\n",
    "```\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['acc'])\n",
    "model.summary()\n",
    "```\n",
    "### onehot编码的多分类\n",
    "```\n",
    "train_label_onehot = tf.keras.utils.to_categorical(train_label)\n",
    "test_label_onehot = tf.keras.utils.to_categorical(test_label)\n",
    "print(train_label_onehot)\n",
    "print(test_label_onehot)\n",
    "\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['acc'])\n",
    "model.summary()\n",
    "```\n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_image,train_label),(test_image,test_label) = tf.keras.datasets.fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image = train_image/255\n",
    "test_image = test_image/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nflatten (Flatten)            (None, 784)               0         \n_________________________________________________________________\ndense_3 (Dense)              (None, 128)               100480    \n_________________________________________________________________\ndense_4 (Dense)              (None, 10)                1290      \n=================================================================\nTotal params: 101,770\nTrainable params: 101,770\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/20\n",
      "1875/1875 [==============================] - 2s 889us/step - loss: 0.4955 - acc: 0.8258\n",
      "Epoch 2/20\n",
      "1875/1875 [==============================] - 2s 872us/step - loss: 0.3757 - acc: 0.8633\n",
      "Epoch 3/20\n",
      "1875/1875 [==============================] - 2s 810us/step - loss: 0.3384 - acc: 0.8762\n",
      "Epoch 4/20\n",
      "1875/1875 [==============================] - 2s 879us/step - loss: 0.3136 - acc: 0.8846\n",
      "Epoch 5/20\n",
      "1875/1875 [==============================] - 2s 922us/step - loss: 0.2954 - acc: 0.8905\n",
      "Epoch 6/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2774 - acc: 0.8963\n",
      "Epoch 7/20\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.2675 - acc: 0.9002\n",
      "Epoch 8/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2571 - acc: 0.9043\n",
      "Epoch 9/20\n",
      "1875/1875 [==============================] - 2s 968us/step - loss: 0.2459 - acc: 0.9078\n",
      "Epoch 10/20\n",
      "1875/1875 [==============================] - 2s 974us/step - loss: 0.2399 - acc: 0.9093\n",
      "Epoch 11/20\n",
      "1875/1875 [==============================] - 2s 987us/step - loss: 0.2306 - acc: 0.9139\n",
      "Epoch 12/20\n",
      "1875/1875 [==============================] - 2s 986us/step - loss: 0.2228 - acc: 0.9163\n",
      "Epoch 13/20\n",
      "1875/1875 [==============================] - 2s 951us/step - loss: 0.2162 - acc: 0.9190\n",
      "Epoch 14/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2075 - acc: 0.9213\n",
      "Epoch 15/20\n",
      "1875/1875 [==============================] - 2s 994us/step - loss: 0.2043 - acc: 0.9233\n",
      "Epoch 16/20\n",
      "1875/1875 [==============================] - 2s 949us/step - loss: 0.1967 - acc: 0.9254\n",
      "Epoch 17/20\n",
      "1875/1875 [==============================] - 2s 946us/step - loss: 0.1918 - acc: 0.9275\n",
      "Epoch 18/20\n",
      "1875/1875 [==============================] - 2s 878us/step - loss: 0.1854 - acc: 0.9304\n",
      "Epoch 19/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1817 - acc: 0.9310\n",
      "Epoch 20/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1774 - acc: 0.9327\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x15c754e1e20>"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "model.fit(train_image,train_label,epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "313/313 [==============================] - 0s 691us/step - loss: 0.3613 - acc: 0.8843\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[0.3613106310367584, 0.8842999935150146]"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "model.evaluate(test_image,test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[0. 0. 0. ... 0. 0. 1.]\n [1. 0. 0. ... 0. 0. 0.]\n [1. 0. 0. ... 0. 0. 0.]\n ...\n [0. 0. 0. ... 0. 0. 0.]\n [1. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]]\n[[0. 0. 0. ... 0. 0. 1.]\n [0. 0. 1. ... 0. 0. 0.]\n [0. 1. 0. ... 0. 0. 0.]\n ...\n [0. 0. 0. ... 0. 1. 0.]\n [0. 1. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "train_label_onehot = tf.keras.utils.to_categorical(train_label)\n",
    "test_label_onehot = tf.keras.utils.to_categorical(test_label)\n",
    "print(train_label_onehot)\n",
    "print(test_label_onehot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nflatten_1 (Flatten)          (None, 784)               0         \n_________________________________________________________________\ndense_5 (Dense)              (None, 128)               100480    \n_________________________________________________________________\ndense_6 (Dense)              (None, 10)                1290      \n=================================================================\nTotal params: 101,770\nTrainable params: 101,770\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1748 - acc: 0.9345\n",
      "Epoch 2/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1722 - acc: 0.9353\n",
      "Epoch 3/20\n",
      "1875/1875 [==============================] - 2s 966us/step - loss: 0.1681 - acc: 0.9368\n",
      "Epoch 4/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1638 - acc: 0.9391\n",
      "Epoch 5/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1606 - acc: 0.9398\n",
      "Epoch 6/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1566 - acc: 0.9407\n",
      "Epoch 7/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1544 - acc: 0.9427\n",
      "Epoch 8/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1491 - acc: 0.9439\n",
      "Epoch 9/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1459 - acc: 0.9456\n",
      "Epoch 10/20\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.1430 - acc: 0.9460\n",
      "Epoch 11/20\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.1418 - acc: 0.9473\n",
      "Epoch 12/20\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.1361 - acc: 0.9503\n",
      "Epoch 13/20\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1351 - acc: 0.9493\n",
      "Epoch 14/20\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.1320 - acc: 0.9508\n",
      "Epoch 15/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1287 - acc: 0.9518\n",
      "Epoch 16/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1260 - acc: 0.9519\n",
      "Epoch 17/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1244 - acc: 0.9529\n",
      "Epoch 18/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1197 - acc: 0.9552\n",
      "Epoch 19/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1199 - acc: 0.9554\n",
      "Epoch 20/20\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1168 - acc: 0.9566\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_image,train_label_onehot,epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "313/313 [==============================] - 0s 758us/step - loss: 0.4670 - acc: 0.8772\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[0.46704521775245667, 0.8772000074386597]"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "model.evaluate(test_image,test_label_onehot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15c77e23760>]"
      ]
     },
     "metadata": {},
     "execution_count": 22
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "history.history.keys()\n",
    "plt.plot(history.epoch,history.history.get('loss'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15c77e42f40>]"
      ]
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "plt.plot(history.epoch,history.history.get('acc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}